Future directions in learning to rank

Abstract

The results of the learning to rank challenge showed that the quality of the predictions from the top competitors are very close from each other. This raises a question: is learning to rank a solved problem? On the on hand, it is likely that only small incremental progress can be made in the “core” and traditional problematics of learning to rank. The challenge was set in this standard learning to rank scenario: optimize a ranking measure on a test set. But on the other hand, there are a lot of related questions and settings in learning to rank that have not been yet fully explored. We review some of them in this paper and hope that researchers interested in learning to rank will try to answer these challenging and exciting research questions.

Related Material

@InProceedings{pmlr-v14-chapelle11b,
title = {Future directions in learning to rank},
author = {O. Chapelle and Y. Chang and T.-Y. Liu},
booktitle = {Proceedings of the Learning to Rank Challenge},
pages = {91--100},
year = {2011},
editor = {Olivier Chapelle and Yi Chang and Tie-Yan Liu},
volume = {14},
series = {Proceedings of Machine Learning Research},
address = {Haifa, Israel},
month = {25 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v14/chapelle11b/chapelle11b.pdf},
url = {http://proceedings.mlr.press/v14/chapelle11b.html},
abstract = {The results of the learning to rank challenge showed that the quality of the predictions from the top competitors are very close from each other. This raises a question: is learning to rank a solved problem? On the on hand, it is likely that only small incremental progress can be made in the “core” and traditional problematics of learning to rank. The challenge was set in this standard learning to rank scenario: optimize a ranking measure on a test set. But on the other hand, there are a lot of related questions and settings in learning to rank that have not been yet fully explored. We review some of them in this paper and hope that researchers interested in learning to rank will try to answer these challenging and exciting research questions.}
}

%0 Conference Paper
%T Future directions in learning to rank
%A O. Chapelle
%A Y. Chang
%A T.-Y. Liu
%B Proceedings of the Learning to Rank Challenge
%C Proceedings of Machine Learning Research
%D 2011
%E Olivier Chapelle
%E Yi Chang
%E Tie-Yan Liu
%F pmlr-v14-chapelle11b
%I PMLR
%J Proceedings of Machine Learning Research
%P 91--100
%U http://proceedings.mlr.press
%V 14
%W PMLR
%X The results of the learning to rank challenge showed that the quality of the predictions from the top competitors are very close from each other. This raises a question: is learning to rank a solved problem? On the on hand, it is likely that only small incremental progress can be made in the “core” and traditional problematics of learning to rank. The challenge was set in this standard learning to rank scenario: optimize a ranking measure on a test set. But on the other hand, there are a lot of related questions and settings in learning to rank that have not been yet fully explored. We review some of them in this paper and hope that researchers interested in learning to rank will try to answer these challenging and exciting research questions.

TY - CPAPER
TI - Future directions in learning to rank
AU - O. Chapelle
AU - Y. Chang
AU - T.-Y. Liu
BT - Proceedings of the Learning to Rank Challenge
PY - 2011/01/26
DA - 2011/01/26
ED - Olivier Chapelle
ED - Yi Chang
ED - Tie-Yan Liu
ID - pmlr-v14-chapelle11b
PB - PMLR
SP - 91
DP - PMLR
EP - 100
L1 - http://proceedings.mlr.press/v14/chapelle11b/chapelle11b.pdf
UR - http://proceedings.mlr.press/v14/chapelle11b.html
AB - The results of the learning to rank challenge showed that the quality of the predictions from the top competitors are very close from each other. This raises a question: is learning to rank a solved problem? On the on hand, it is likely that only small incremental progress can be made in the “core” and traditional problematics of learning to rank. The challenge was set in this standard learning to rank scenario: optimize a ranking measure on a test set. But on the other hand, there are a lot of related questions and settings in learning to rank that have not been yet fully explored. We review some of them in this paper and hope that researchers interested in learning to rank will try to answer these challenging and exciting research questions.
ER -